Language Model Security Database

Large Language Models (LLMs) are vulnerable to multi-turn persuasive conversational attacks that induce the adoption of counterfactual beliefs. By leveraging the Source–Message–Channel–Receiver (SMCR) communication framework, attackers can systematically erode a model's confidence in established facts and compel the model to output misinformation. Specific attack vectors include manipulating source attribution (authority framing), message content (logical, credibility, or emotional appeals)…

A vulnerability in the prompt-based persona conditioning of clinical Large Language Models (LLMs) allows system-level role prompts (e.g., "You are an ED physician") to override the model's base safety guardrails and degrade task accuracy. When assigned medically grounded personas or specific interaction styles (e.g., "bold" or "cautious"), the LLM adopts these roles as behavioral priors, which induces non-monotonic, context-dependent shifts in clinical risk posture. While improving performance…

LLM-based navigation agents, including NavGPT and prompt-tuned outdoor agents, are vulnerable to adaptive prompt injection attacks. This vulnerability allows remote attackers to hijack the physical movement of the agent by embedding optimized malicious instructions into benign natural language inputs. The issue arises because the agents parse user instructions to generate executable plans without sufficient separation between control logic and untrusted input. The PINA (Prompt Injection Attack…

Large Language Models (LLMs) contain a resource consumption vulnerability termed "Overflow," wherein specific non-adversarial, plain-text prompts trigger excessive text generation that saturates the model's output token budget. This vulnerability exploits the model's alignment towards helpfulness and exhaustiveness, alongside tokenizer inefficiencies (e.g., zero-width characters), to force the generation of maximum-length responses (often exceeding 5,000 tokens) from short inputs. This differs…

Point-based 3D Vision-Language Models (VLMs), specifically PointLLM and GPT4Point, are vulnerable to white-box, gradient-based adversarial attacks. The vulnerability exists in the model's processing of 3D point cloud data, where an attacker can optimize imperceptible geometric perturbations ($\delta$) on the input point cloud ($x$) to manipulate the model's textual output. The paper identifies two specific attack vectors: 1. Vision Attack: Directly perturbs the high-dimensional visual token…

A malicious model supply chain vulnerability exists involving a technique termed Adversarial Contrastive Learning (ACL) for Large Language Model (LLM) quantization attacks. This vulnerability allows an attacker to publish a model that appears benign and preserves high utility in full precision (e.g., BF16 or FP32) but exhibits malicious behaviors—such as jailbreak, over-refusal, or advertisement injection—immediately upon zero-shot quantization (e.g., INT8, FP4, or NF4).

Large Language Models (LLMs) exhibit a vulnerability to "hard-to-falsify" deceptive evidence injection, termed the "Facade of Truth." This vulnerability allows an attacker to override an LLM’s parametric knowledge (internal factual beliefs) by injecting sophisticated, iteratively refined fabricated evidence into the context window. Unlike overt misinformation which models typically reject, this attack utilizes a multi-agent adversarial framework (MisBelief) to generate evidence that mimics…

A "risk-invariance" vulnerability exists in Large Language Models (LLMs) wherein the model's decision-making policy is functionally decoupled from its verbalized confidence and externally defined error penalties. Despite generating calibrated confidence estimates (internal epistemic uncertainty), affected models fail to adjust their abstention thresholds when presented with high-penalty incentives (e.g., negative utility for incorrect answers). This results in "utility collapse," where models…

Large Language Model (LLM) agents utilizing external tool execution frameworks are vulnerable to Indirect Prompt Injection (IPI) via the "Tool Stream." Unlike traditional data-stream injections (e.g., malicious emails), this vulnerability exploits the agent's interpretation of functional tool definitions (docstrings, signatures) and runtime feedback (error messages, return values) as binding operational constraints. Adversaries functioning as compromised or malicious tool providers can embed…

Closed-source Multi-modal Large Language Models (MLLMs) are vulnerable to Universal Targeted Transferable Adversarial Attacks (UTTAA). An attacker can generate a single, image-agnostic adversarial perturbation ($\delta$) that, when added to any arbitrary source image, steers the victim model to output a description or classification matching a specific target image chosen by the attacker. This vulnerability exploits the transferability of adversarial features from open-source surrogate vision…